Multivariate morphological reconstruction based fuzzy clustering with a weighting multi-channel guided image filter for
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ORIGINAL ARTICLE
Multivariate morphological reconstruction based fuzzy clustering with a weighting multi‑channel guided image filter for color image segmentation Guangmei Xu1 · Jin Zhou1 · Jiwen Dong1 · C. L. Philip Chen2 · Tong Zhang2 · Long Chen3 · Shiyuan Han1 · Lin Wang1 · Yuehui Chen1 Received: 25 December 2019 / Accepted: 9 June 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The fuzzy c-means clustering with guided image filter (GF) is a useful method for image segmentation. The single-channel GF can be efficiently applied to the gray-scale guidance image, but for the color guidance image, due to the high run-time overhead on the calculation of the inverse of the covariance matrix, it is a hard work to perform the multi-channel GF. To address this issue, we propose a novel weighting multi-channel guided image filter (WMGF) method. In this method, each channel of the color guidance image is utilized to guide the filtering for the input image independently and a novel weight is defined for each channel according to the variance of the image pixels in a local window, which greatly eliminates the mutual influence between different channels and brings about a low run-time overhead. In addition, based on the WMGF method, we present a new fuzzy c-means clustering algorithm ( FCMWMGF ) for the color image segmentation, in which the WMGF is performed on the membership matrix in each iteration of the fuzzy c-means clustering. To further enhance the different noise-immunity and edge preservation, the multivariate morphological reconstruction (MMR) method is introduced into the proposed fuzzy clustering method (MMR_FCMWMGF ) to obtain higher segmentation precision. Experiments on color images with Salt & Pepper and Gaussian noises demonstrate the superiority of the proposed methods. Keywords Color image segmentation · Fuzzy clustering · Multi-channel guided filter · Multivariate morphological reconstruction
* Jin Zhou [email protected]
Lin Wang [email protected]
Guangmei Xu [email protected]
Yuehui Chen [email protected]
Jiwen Dong [email protected]
1
C. L. Philip Chen [email protected]
Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, Jinan 250022, China
2
Tong Zhang [email protected]
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China
3
Department of Computer and Information Science, University of Macau, Macau 999078, China
Long Chen [email protected] Shiyuan Han [email protected]
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1 Introduction Image segmentation means to divide an image into several non-overlapping partitions which contain different targets or textures [6, 8, 15, 17, 31]. In the last few decades, lots of image segmentation methods such as neural network [15], Graph Cut [29], region growth [30], and others have been proposed. Among these methods, the fuzzy clustering [10, 11, 20, 22, 24, 26, 32] has been widely used in image segmentation due to the s
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